Instant messaging (IM) has become part of everyday life and its users require IM availability in many different situations. IM is commonly mediated through a broad range of devices including computers, tablets, and cell phones. However, these devices are not very appropriate for in-car use. Their handling can require too much attention from the user, which results in dangerous driver distraction.
Of course, instant messaging is just one specific example of a more general class of textual message applications. Other specific examples of textual message applications include without limitation cell phone text messages, email, and/or social media messaging (e.g., Twitter, LinkedIn, Facebook etc.). The discussion that follows should be understood to apply broadly to this general class of textual message applications using the specific example of instant messaging.
Searching or browsing past messages—emails or instant messages, etc.—is a tedious task, especially if it has to be done in a hands-free, eyes free manner as while driving in a car. It is complicated to narrow the search results sufficiently and present to the user efficiently and safely. Searching by content requires entering a search term that needs to match exactly. If only the topic of the message session is known but not the actual keyword for the search, the message can't be retrieved. Say a message contains the word “automotive” but the search is defined for a key term “car”, an explicit search wouldn't match. In addition, the results of searching by keywords often are too wide. To further filter results, it would be advantageous to be able to query by the situation in which it was written, e.g. “present all instant messages on topic ‘car’ that I dictated on my way from Merelbeke to Aachen”.
In systems using automatic speech recognition, using an unspecific (factory) language model to capture the driver's message dictation wastes recognition accuracy potential; it doesn't model statistics specific to the user—his choice of topics and words—nor his current situation, e.g. when driving in the mountains in January, messages on winter sports are more likely than others.